Deep-learning two-photon fiberscopy for video-rate brain imaging in freely-behaving mice

Updated Toolbox for Assessing Neuronal Network Reconstruction after Cell Therapy

Cell therapy has proven to be a promising treatment for a range of neurological disorders, including Parkinson Disease, drug-resistant epilepsy, and stroke, by restoring function after brain damage. Nevertheless, evaluating the true effectiveness of these therapeutic interventions requires a deep understanding of the functional integration of grafted cells into existing neural networks. This review explores a powerful arsenal of molecular techniques revolutionizing our ability to unveil functional integration of grafted cells within the host brain. From precise manipulation of neuronal activity to pinpoint the functional contribution of transplanted cells by using opto- and chemo-genetics, to real-time monitoring of neuronal dynamics shedding light on functional connectivity within the reconstructed circuits by using genetically encoded (calcium) indicators in vivo. Finally, structural reconstruction and mapping communication pathways between grafted and host neurons can be achieved by monosynaptic tracing with viral vectors. The cutting-edge toolbox presented here holds immense promise for elucidating the impact of cell therapy on neural circuitry and guiding the development of more effective treatments for neurological disorders.

A Deep Learning Approach for Improving Two-Photon Vascular Imaging Speeds

A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based (PSSR Res-U-Net architecture) algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to images with large fields of view from a mouse model and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pretrained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings.

Deep learning in mesoscale brain image analysis: A review

Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, their high noise levels, the complex morphology of the brain from the cellular to the regional and anatomical levels, the inhomogeneous distribution of fluorescent labels in the cells and tissues, and imaging artifacts. Due to their impressive ability to extract relevant information from images, deep learning algorithms are widely applied to microscopy images of the brain to address these challenges and they perform superiorly in a wide range of microscopy image processing and analysis tasks. This article reviews the applications of deep learning algorithms in brain mesoscale microscopy image processing and analysis, including image synthesis, image segmentation, object detection, and neuron reconstruction and analysis. We also discuss the difficulties of each task and possible directions for further research.

Overcome the "Buckets Effect": Integration of AIEgens into Proteins for Fluorescence-Enhanced Two-Photon Imaging

Luminogens with aggregation-induced emission characteristics (AIEgens) are considered good options for two-photon (2P) probes, owing to their flexibility of design, heavy-metal-free composition, and resistance to photobleaching. However, the design principles for large 2P absorption cross-section (δ) generally require high coplanarity, strong donor-acceptor (D-A) interactions, and long conjugation, which can severely weaken the brightness of AIEgens at the aggregated state and undermine their potential in 2P fluorescence imaging (2PFI). Exploration of a feasible approach to overcome the "Buckets Effect" of AIEgen-based 2P probes is thus a fascinating yet challenging task. Herein, an AIEgen, namely (Z)-2-(4-aminophenyl)-3-(5-(4-(bis(4-methoxyphenyl)amino)phenyl)thiophen-2-yl)acrylonitrile (MTAA) is designed to have a big δ according to the calculation result and a low fluorescence quantum yield (QY) of 2.2% in dimethyl sulfoxide (DMSO). Impressively, upon integrating into bovine serum albumin (BSA), the protein-sized MTAA@BSA dots exhibit a 25-fold higher fluorescence QY compared to MTAA molecules, contributing to an imaging depth of 818 µm in the brain vasculature. The retention and clearance of MTAA@BSA dots in the liver and kidney are also studied using 2PFI. Overall, this work provides a facile approach to overcome the "Buckets Effect" of AIEgen to generate highly efficient, reliable, and biocompatible 2P probes.

Small-molecule fluorogenic probes for mitochondrial nanoscale imaging

Mitochondria are inextricably linked to the development of diseases and cell metabolism disorders. Super-resolution imaging (SRI) is crucial in enhancing our understanding of mitochondrial ultrafine structures and functions. In addition to high-precision instruments, super-resolution microscopy relies heavily on fluorescent materials with unique photophysical properties. Small-molecule fluorogenic probes (SMFPs) have excellent properties that make them ideal for mitochondrial SRI. This paper summarizes recent advances in the field of SMFPs, with a focus on the chemical and spectroscopic properties required for mitochondrial SRI. Finally, we discuss future challenges in this field, including the design principles of SMFPs and nanoscopic techniques.

Three-dimensional-generator U-net for dual-resonant scanning multiphoton microscopy image inpainting and denoising

A dual-resonant scanning multiphoton (DRSM) microscope incorporating a tunable acoustic gradient index of refraction lens and a resonant mirror is developed for rapid volumetric bioimaging. It is shown that the microscope achieves a volumetric imaging rate up to 31.25 volumes per second (vps) for a scanning volume of up to 200 × 200 × 100 µm³ with 256 × 256 × 128 voxels. However, the volumetric images have a severe negative signal-to-noise ratio (SNR) as a result of a large number of missing voxels for a large scanning volume and the presence of Lissajous patterning residuals. Thus, a modified three-dimensional (3D)-generator U-Net model trained using simulated microbead images is proposed and used to inpaint and denoise the images. The performance of the 3D U-Net model for bioimaging applications is enhanced by training the model with high-SNR in-vitro drosophila brain images captured using a conventional point scanning multiphoton microscope. The trained model shows the ability to produce clear in-vitro drosophila brain images at a rate of 31.25 vps with a SNR improvement of approximately 20 dB over the original images obtained by the DRSM microscope. The training convergence time of the modified U-Net model is just half that of a general 3D U-Net model. The model thus has significant potential for 3D in-vivo bioimaging transfer learning. Through the assistance of transfer learning, the model can be extended to the restoration of in-vivo drosophila brain images with a high image quality and a rapid training time.

Label-free highly multimodal nonlinear endoscope

We demonstrate a 2 mm diameter highly multimodal nonlinear micro-endoscope allowing label-free imaging of biological tissues. The endoscope performs multiphoton fluorescence (3-photon, 2-photon), harmonic generation (second-SHG and third-THG) and coherent anti-Stokes Raman scattering (CARS) imaging over a field of view of 200 µm. The micro-endoscope is based on a double-clad antiresonant hollow core fiber featuring a high transmission window (850 nm to 1800 nm) that is functionalized with a short piece of graded-index (GRIN) fiber. When combined with a GRIN micro-objective, the micro-endoscope achieves a 1.1 µm point spread function (PSF). We demonstrate 3-photon, 2-photon, THG, SHG, and CARS high resolution images of unlabelled biological tissues.

Transgenic mice encoding modern imaging probes: Properties and applications

Modern biology is increasingly reliant on optical technologies, including visualization and longitudinal monitoring of cellular processes. The major limitation here is the availability of animal models to track the molecules and cells in their natural environment in vivo. Owing to the integrity of the studied tissue and the high stability of transgene expression throughout life, transgenic mice encoding fluorescent proteins and biosensors represent unique tools for in vivo studies in norm and pathology. We review the strategies for targeting probe expression in specific tissues, cell subtypes, or cellular compartments. We describe the application of transgenic mice expressing fluorescent proteins for tracking protein expression patterns, apoptotic events, tissue differentiation and regeneration, neurogenesis, tumorigenesis, and cell fate mapping. We overview the possibilities of functional imaging of secondary messengers, neurotransmitters, and ion fluxes. Finally, we provide the rationale and perspectives for the use of transgenic imaging probes in translational research and drug discovery.